Globalization is driving unprecedented levels of scale, agility, and execution of organizations, such as manufacturing, financial services, and others, increasing their potential exposure to a disruption at one of their third parties (e.g., suppliers, vendors, partners, etc.) including possibly within their global supply chain. Exposure to disruption highlights the need for enhanced methodologies for global supply chain management, business partnerships, or any other third party risk management, and risk management overall. Retroactive analysis techniques have historically been used to provide sufficient methodologies for organizations to make adjustments to procurement and other third party relationships to support going-forward operations and to deliver products and services. However, the ability to manage operations using retroactive analysis techniques is less effective given the global nature and speed of business. Capital markets and private owners alike now expect significantly increased levels of forecasting, scale, consistency, precision, and speed. The performance expectations range from financial to operational.
Performance is driven by a constant stream of actionable decisions. Those decisions are made through a) human intervention, b) machines, or c) a combination thereof. The decisions rely on available data, structured and unstructured. For example, organizations, such as automobile companies, are sourcing thousands of goods from suppliers and service providers across the globe to produce products. Often, these goods are being delivered via fragile transportation networks on a “just-in-time” basis. As a result, the risk of a supplier or other third parties being unable to produce and deliver the goods on a timely basis is increased, and the actual inability for a producer to produce and deliver goods may cause significant production problems for the end-producer. Similarly, other types of organizations, such as financial services firms, for example, face significant risks from their third parties as organizations increasingly rely on outside entities (e.g., third-party vendors) for operations. The resources in charge of identifying risks to suppliers (e.g., risk managers) must identify risks that stem from a combination of factors within their control (e.g., financial, operational) and outside of their control (e.g., geopolitical events, economic conditions, severe weather, compromised infrastructure, etc.), and make a necessary set of related decisions to maintain expected production levels.
Risk managers must also identify risks to third parties that stem from factors outside their control. As an example, shortages of raw materials (e.g., silicone, rubber, rare earth metals, etc.) for a producer of components that are used in a final product could cause significant production problems for a manufacturer of the final product. As another example, geopolitical risks, such as regional violence, strikes, or other geopolitical situations can cause suppliers or vendors in the region to have production or distribution problems. Still yet, weather situations, such as hurricanes and typhoons, droughts, snow storms, and other weather situations can cause third party disruptions. Infrastructure problems, be it related to transportation or communication or power, within a geographic region can also cause risk to third parties of a user (e.g., manufacturer or general contractor) as third parties may experience potential problems related to the movement of goods or people. For example, a breakdown in the communications infrastructure of a geographic region may in fact have severe implications for a financial services firm with third parties operating in the region.
As a result of the potential fragility of third parties, such as suppliers in global supply chains, and the increased risk associated with them due to numerous factors including just-in-time inventories and scale, risk managers and other users need a solution to holistically monitor and forecast discrete and macro risk that is able to view third party financial and performance risk in the broader context of the geopolitical factors that can potentially disrupt the movement of critical goods and services.
Conventional techniques for performing forecasting have shortcomings due to information analysis techniques and technologies. Many organizations monitor events, such as news stories and other sources, without any additional context or analysis. For example, the organizations may be alerted to a weather event, an accident or criminal act in a geographic region. But, this type of information alert is actually detrimental, as over time, organizations become desensitized to alerts and the alerts become “noise,” especially since further contextual analysis may be needed in response to those alerts. Hence, an improved signal processing and risk analytic solution that filters signals from noise provides insights and information being processed to yield results for organizations to forecast risk from the dynamically filtered signals is needed. This enables organizations to see a more complete picture of the risks to which they are exposed, including those interconnected risks to which the organizations may now be more susceptible.